Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description should carry the burden of behavioral disclosure. It says 'Validate' but does not describe return behavior (e.g., boolean, error messages, or side effects). The lack of detail on what constitutes 'consistent' or what happens on failure reduces transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two short sentences, containing no redundant information. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has three parameters with no schema descriptions and no output schema. The description only covers the basic purpose, leaving out crucial details about input format requirements, validation outcomes, error handling, and return data. For a validation tool, this is insufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. However, it does not mention any of the three parameters (text, delimiter, has_header) or their roles. The schema provides names and defaults, but the description adds no meaning beyond what the tool name implies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates CSV structure, focusing on consistent column counts and header. It distinguishes itself from sibling tools like csv_to_json and csv_validate_batch, and uses specific verbs 'Validate' and 'Check'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a 'When' clause specifying to use the tool before import for column consistency checks. It does not explicitly mention when not to use it, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.